Method and apparatus with battery state estimation

ABSTRACT

A processor-implemented method with battery state estimation includes: determining a state variation of a battery using a voltage difference between a sensed voltage of the battery and an estimated voltage of the battery that is estimated by an electrochemical model corresponding to the battery; updating an internal state of the electrochemical model based on the determined state variation of the battery; and estimating state information of the battery based on the updated internal state of the electrochemical model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a division of application Ser. No. 16/814,212 filedon Mar. 10, 2020, which claims the benefit under 35 USC § 119(a) ofKorean Patent Application No. 10-2019-0131570 filed on Oct. 22, 2019, inthe Korean Intellectual Property Office, the entire disclosure of whichis incorporated herein by reference for all purposes.

BACKGROUND 1. Field

The following description relates to a method and apparatus with batterystate estimation.

2. Description of Related Art

There are various methods to estimate a state of a battery. For example,such methods may include estimating a state of a battery by integratingan electrical current of a battery or using a battery model, forexample, an electric circuit model or an electrochemical model.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

In one general aspect, a processor-implemented method with battery stateestimation includes: determining a state variation of a battery using avoltage difference between a sensed voltage of the battery and anestimated voltage of the battery that is estimated by an electrochemicalmodel corresponding to the battery; updating an internal state of theelectrochemical model based on the determined state variation of thebattery; and estimating state information of the battery based on theupdated internal state of the electrochemical model.

The determining of the state variation of the battery may includedetermining the state variation of the battery based on the voltagedifference, previous state information previously estimated by theelectrochemical model, and an open-circuit voltage (OCV) table.

The determining of the state variation of the battery may furtherinclude obtaining an OCV corresponding to the previous state informationbased on the OCV table, and applying the voltage difference to theobtained OCV.

The updating of the internal state of the electrochemical model mayinclude correcting an ion concentration distribution in an activematerial particle or an ion concentration distribution in an electrodebased on the determined state variation of the battery.

The updating of the internal state of the electrochemical model mayinclude uniformly correcting an ion concentration distribution in anactive material particle or an ion concentration distribution in anelectrode based on the determined state variation of the battery.

The updating of the internal state of the electrochemical model mayinclude determining a concentration gradient characteristic based on adiffusion characteristic based on the determined state variation of thebattery, and correcting an ion concentration distribution of the batterybased on the determined concentration gradient characteristic.

The updating of the internal state of the electrochemical model mayfurther include calculating a diffusion equation of an active materialbased on the determined state variation of the battery, and correctingan ion concentration distribution in an active material particle or anion concentration distribution in the electrode.

The internal state of the electrochemical model may include any one orany combination of any two or more of a positive electrode lithium-ionconcentration distribution of the battery, a negative electrodelithium-ion concentration distribution of the battery, and anelectrolyte lithium-ion concentration distribution of the battery.

The method may further include: verifying whether the voltage differencebetween the sensed voltage of the battery and the estimated voltage ofthe battery exceeds a threshold voltage difference.

The electrochemical model may be configured to estimate stateinformation of a target battery among a plurality of batteries. Thesensed voltage may be a voltage measured from the target battery. Theestimated voltage may be a voltage previously estimated from anotherbattery among the plurality of batteries by the electrochemical model.

The battery may be a battery cell, a battery module, or a battery pack.

The estimated state information of the battery may include any one orany combination of any two or more of a state of charge (SOC), a stateof heath (SOH), and abnormality state information.

In another general aspect, a non-transitory computer-readable storagemedium stores instructions that, when executed by a processor, cause theprocessor to perform the method described above.

In another general aspect, a processor-implemented method with batterystate estimation includes: obtaining sensing data including a sensedvoltage of a battery; obtaining an estimated voltage of the battery fromthe sensing data using an electrochemical model corresponding to thebattery; calculating a first voltage difference between the sensedvoltage of the battery and the estimated voltage of the battery;selecting a correction method of the electrochemical model based on thefirst voltage difference; correcting an internal state of theelectrochemical model or a sensed current to be input to theelectrochemical model by applying the selected correction method to theelectrochemical model; and estimating state information of the batteryusing the electrochemical model to which the correction method isapplied.

The correcting of the internal state of the electrochemical model or thesensed current to be input to the electrochemical model based on theselected correction method may include: updating the internal state ofthe electrochemical model using a state variation of the batterydetermined by the first voltage difference between a voltage of thebattery sensed in a current time period and a voltage of the batteryestimated by the electrochemical model; or correcting a sensed currentof the battery in the current time period to be input to theelectrochemical model using a capacity error corresponding to a secondvoltage difference between a sensed voltage of the battery in a previoustime period and an estimated voltage of the battery in the previous timeperiod.

The selecting of the correction method of the electrochemical modelbased on the first voltage difference may include: in response to thefirst voltage difference being greater than a threshold voltagedifference, selecting a correction method of correcting the internalstate of the electrochemical model; and in response to the first voltagedifference being less than or equal to the threshold voltage difference,selecting a correction method of correcting the sensed current to beinput to the electrochemical model.

The selecting of the correction method of the electrochemical model mayinclude selecting the correction method of the electrochemical modelsuch that a correction method of correcting the sensed current to beinput to the electrochemical model is to be performed more frequentlythan a correction method of correcting the internal state of theelectrochemical model.

In another general aspect, a non-transitory computer-readable storagemedium may store instructions that, when executed by a processor, causethe processor to perform the method described above.

In another general aspect, an apparatus with battery state estimationincludes: a processor configured to determine a state variation of abattery using a voltage difference between a sensed voltage of thebattery and an estimated voltage of the battery that is estimated by astored electrochemical model corresponding to the battery, update aninternal state of the electrochemical model based on the determinedstate variation, and estimate state information of the battery based onthe updated internal state of the electrochemical model.

The processor may be further configured to determine the state variationof the battery based on the voltage difference, previous stateinformation that is previously estimated by the electrochemical model,and an open-circuit voltage (OCV) table.

The processor may be further configured to determine the state variationof the battery by obtaining an OCV corresponding to the previous stateinformation based on the OCV table and applying the voltage differenceto the obtained OCV.

The processor may be further configured to update the internal state ofthe electrochemical model by correcting an ion concentrationdistribution in an active material particle or an ion concentrationdistribution in an electrode based on the determined state variation ofthe battery.

The processor may be further configured to update the internal state ofthe electrochemical model by uniformly correcting an ion concentrationdistribution in an active material particle or an ion concentrationdistribution in an electrode based on the determined state variation ofthe battery.

The processor may be further configured to: update the internal state ofthe electrochemical model by determining a concentration gradientcharacteristic based on a diffusion characteristic based on thedetermined state variation of the battery, and by correcting an ionconcentration distribution in an active material particle or an ionconcentration distribution in an electrode based on the determinedconcentration gradient characteristic.

The electrochemical model may be configured to estimate stateinformation of a target battery among a plurality of batteries. Thesensed voltage may be a voltage measured from the target battery. Theestimated voltage may be a voltage previously estimated from anotherbattery among the plurality of batteries by the electrochemical model.

The apparatus may further include a memory storing the electrochemicalmodel.

The estimated state information of the battery may include any one orany combination of any two or more of a state of charge (SOC), a stateof heath (SOH), and abnormality state information.

The apparatus may be a vehicle or a mobile device, and may be powered bythe battery.

Other features and aspects will be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1 and 2 illustrate an example of a battery system.

FIG. 3 illustrates an example of estimating a state of a battery.

FIGS. 4 and 5 illustrate an example of estimating a state of a battery.

FIG. 6 illustrates an example of a battery model.

FIGS. 7 and 8 illustrate examples of determining a state variation of abattery.

FIGS. 9, 10 a, 10 b, and 11 illustrate examples of updating an internalstate of a battery model.

FIGS. 12a and 12b illustrate another example of a method of estimating astate of a battery.

FIG. 13 illustrates an example of an apparatus with battery stateestimation.

FIGS. 14 and 15 illustrate an example of a vehicle embodiment.

FIG. 16 illustrates an example of a mobile device embodiment.

Throughout the drawings and the detailed description, the same referencenumerals refer to the same elements. The drawings may not be to scale,and the relative size, proportions, and depiction of elements in thedrawings may be exaggerated for clarity, illustration, and convenience.

DETAILED DESCRIPTION

The following detailed description is provided to assist the reader ingaining a comprehensive understanding of the methods, apparatuses,and/or systems described herein. However, various changes,modifications, and equivalents of the methods, apparatuses, and/orsystems described herein will be apparent after an understanding of thedisclosure of this application. For example, the sequences of operationsdescribed herein are merely examples, and are not limited to those setforth herein, but may be changed as will be apparent after anunderstanding of the disclosure of this application, with the exceptionof operations necessarily occurring in a certain order.

The features described herein may be embodied in different forms and arenot to be construed as being limited to the examples described herein.Rather, the examples described herein have been provided merely toillustrate some of the many possible ways of implementing the methods,apparatuses, and/or systems described herein that will be apparent afteran understanding of the disclosure of this application.

Herein, it is noted that use of the term “may” with respect to anexample or embodiment, e.g., as to what an example or embodiment mayinclude or implement, means that at least one example or embodimentexists in which such a feature is included or implemented while allexamples and embodiments are not limited thereto.

Throughout the specification, when an element, such as a layer, region,or substrate, is described as being “on,” “connected to,” or “coupledto” another element, it may be directly “on,” “connected to,” or“coupled to” the other element, or there may be one or more otherelements intervening therebetween. In contrast, when an element isdescribed as being “directly on,” “directly connected to,” or “directlycoupled to” another element, there can be no other elements interveningtherebetween.

As used herein, the term “and/or” includes any one and any combinationof any two or more of the associated listed items.

Although terms such as “first,” “second,” and “third” may be used hereinto describe various members, components, regions, layers, or sections,these members, components, regions, layers, or sections are not to belimited by these terms. Rather, these terms are only used to distinguishone member, component, region, layer, or section from another member,component, region, layer, or section. Thus, a first member, component,region, layer, or section referred to in examples described herein mayalso be referred to as a second member, component, region, layer, orsection without departing from the teachings of the examples.

The terminology used herein is for describing various examples only andis not to be used to limit the disclosure. The articles “a,” “an,” and“the” are intended to include the plural forms as well, unless thecontext clearly indicates otherwise. The terms “comprises,” “includes,”and “has” specify the presence of stated features, numbers, operations,members, elements, and/or combinations thereof, but do not preclude thepresence or addition of one or more other features, numbers, operations,members, elements, and/or combinations thereof.

Unless otherwise defined, all terms, including technical and scientificterms, used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this disclosure pertains and basedon an understanding of the disclosure of this application. Terms, suchas those defined in commonly used dictionaries, are to be interpreted ashaving a meaning that is consistent with their meaning in the context ofthe relevant art and the disclosure of this application and are not tobe interpreted in an idealized or overly formal sense unless expresslyso defined herein.

Also, in the description of example embodiments, detailed description ofstructures or functions that are thereby known after an understanding ofthe disclosure of this application will be omitted when it is deemedthat such description will cause ambiguous interpretation of the exampleembodiments.

The features of the examples described herein may be combined in variousways as will be apparent after an understanding of the disclosure ofthis application. Further, although the examples described herein have avariety of configurations, other configurations are possible as will beapparent after an understanding of the disclosure of this application.

FIGS. 1 and 2 illustrate an example of a battery system.

Referring to FIG. 1, a battery system 100 includes, for example, aplurality of batteries 110-1 through 110-n and an apparatus 120 withbattery state estimation. The apparatus 120 with battery stateestimation will be hereinafter simply referred to as a battery stateestimating apparatus 120.

Each of the batteries 110-1 through 110-n may be a battery cell, abattery module, or a battery pack.

The battery state estimating apparatus 120 may sense each of thebatteries 110-1 through 110-n using at least one sensor. That is, thebattery state estimating apparatus 120 may collect sensing data of eachof the batteries 110-1 through 110-n. The sensing data may include, forexample, voltage data, current data, and/or temperature data.

The battery state estimating apparatus 120 may estimate stateinformation of each of the batteries 110-1 through 110-n and output aresult of the estimating. The state information may include, forexample, a state of charge (SOC), a state of heath (SOH), and/orabnormality state information. A battery model used to estimate suchstate information may be, for example, an electrochemical model whichwill be described hereinafter with reference to FIG. 2.

FIG. 2 illustrates an example of estimating the state information usingthe battery model.

Referring to FIG. 2, the battery state estimating apparatus 120 mayestimate state information of a battery 110 using an electrochemicalmodel corresponding to the battery 110. The battery 110 may, forexample, correspond to any one of the batteries 110-1 through 110-ndescribed above with respect to FIG. 1. The electrochemical model may beconfigured to model an internal physical phenomenon, such as, forexample, a potential of a battery and an ion concentration distribution,and estimate state information of the battery.

A level of accuracy in estimating the state information of the battery110 may affect an optimal operation and control of the battery 110. Whenestimating the state information using the electrochemical model, theremay be an error between sensor information that is obtained by a sensorconfigured to measure current, voltage, and temperature data and is tobe input to the electrochemical model, and state information calculatedthrough a modeling method. The error may need to be compensated for orcorrected. The terms “compensated for” and “corrected” may beinterchangeably used herein.

In an example, the battery state estimating apparatus 120 may determinea voltage difference between a sensed voltage of the battery 110 that ismeasured by a sensor and an estimated voltage of the battery 110 that isestimated by the electrochemical model. The battery state estimatingapparatus 120 may then determine a state variation of the battery 110using the determined voltage difference. The battery state estimatingapparatus 120 may then update an internal state (e.g., a potential ofthe battery and/or an ion concentration distribution) of theelectrochemical model based on the determined state variation. Thebattery state estimating apparatus 120 may then estimate stateinformation of the battery 110 based on the updated internal state ofthe electrochemical model. As described, the battery state estimatingapparatus 120 may determine a state variation of a battery such that avoltage difference between a sensed voltage of the battery and anestimated voltage of the battery is to be minimized, and update aninternal state of the electrochemical model. Through such a feedbackstructure, the battery state estimating apparatus 120 may estimateaccurate state information of the battery 110 without increasing a modelcomplexity and an operation or calculation amount.

FIG. 3 is a flowchart illustrating an example of determining stateinformation of a battery by a battery state estimating apparatus. In anexample, a state of a battery may be estimated in a plurality of timeperiods and the battery state estimating apparatus may estimate stateinformation of the battery in each of the time periods. Such examplewill be described as applied to a single cell model for the convenienceof description.

Referring to FIG. 3, in operation 310, the battery state estimatingapparatus collects sensing data of a battery. The sensing data mayinclude, for example, a sensed voltage, a sensed current, and a sensedtemperature. For example, the sensing data may be stored in a form of aprofile indicating a change in magnitude over time.

In operation 320, an estimated voltage of the battery and stateinformation (e.g., SOC) of the battery are determined, for example, byan electrochemical model to which the sensed current and the sensedtemperature are input.

In operation 330, the battery state estimating apparatus calculates avoltage difference between the sensed voltage of the battery and theestimated voltage that is estimated by the electrochemical model. Forexample, the voltage difference may be determined to be a moving averagevoltage for a latest preset time period.

Although not illustrated in FIG. 3, according to an example, the batterystate estimating apparatus may determine whether the state informationof the battery needs to be corrected, based on whether the calculatedvoltage difference exceeds a threshold voltage difference. When an erroroccurs in the electrochemical model, the estimated voltage that isobtained using the electrochemical model may differ from the sensedvoltage of the battery by a significant amount or an amount that resultsin the estimated voltage being excessively inaccurate. Thus, to preventerrors from being accumulated, the battery state estimating apparatusmay determine whether the correcting is needed based on the voltagedifference.

For example, when the calculated voltage difference exceeds thethreshold voltage difference, the battery state estimating apparatus maydetermine that the state information of the battery needs to becorrected, and perform operation 340. In contrast, when the calculatedvoltage difference does not exceed the threshold voltage difference, thebattery state estimating apparatus may determine that the stateinformation of the battery does not need to be corrected, and return tooperation 310 without performing operations 340, 350, and 360.

In operation 340, the battery state estimating apparatus determines astate variation of the battery using the calculated voltage difference.For example, the battery state estimating apparatus may determine thestate variation of the battery based on the calculated voltagedifference, previous state information of the battery, and anopen-circuit voltage (OCV) table. The previous state information of thebattery may be the state information that is previously estimated usingthe electrochemical model in operation 320. For example, the statevariation may include an amount of change in SOC, hereinafter simply anSOC variation, which will be described in greater detail with referenceto FIGS. 7 and 8.

In operation 350, the battery state estimating apparatus updates theelectrochemical model by correcting an internal state of theelectrochemical model based on the state variation of the battery. Forexample, the battery state estimating apparatus may update the internalstate of the electrochemical model by correcting an ion concentrationdistribution in an active material particle or an ion concentrationdistribution in an electrode, based on the state variation of thebattery. In such an example, an active material may include a positiveelectrode and a negative electrode of the battery. The battery stateestimating apparatus estimates the state information of the batteryusing the electrochemical model of which the internal state is updated.Thus, through such a feedback structure by which the battery stateestimating apparatus determines the state variation of the battery tominimize the voltage difference between the sensed voltage of thebattery and the estimated voltage of the battery that is estimated bythe electrochemical model, and then updates the internal state of theelectrochemical model, it is possible to estimate the state informationof the battery more accurately with fewer operations and/orcalculations. A more detailed description of an example of updating theelectrochemical model will follow hereinafter with reference to FIGS. 9through 11.

In operation 360, the battery state estimating apparatus determineswhether to terminate an operation of estimating of a state of thebattery. For example, when a preset operation period has not elapsed,the battery state estimating apparatus returns to operation 310 for anext period. In contrast, when the operation period has elapsed, thebattery state estimating apparatus terminates the operation ofestimating a state of the battery.

In an example embodiment, a battery state estimating apparatus maydetermine state information of a plurality of batteries in each of aplurality of time periods. The battery state estimating apparatus mayselect a target battery, among the plurality of batteries, from whichstate information is to be estimated in each of the time periods usingan electrochemical model. That is, in each of the time periods, thebattery state estimating apparatus may determine state information of atarget battery using the electrochemical model, and determine stateinformation of remaining batteries using a current integration method.The current integration method may be a method of estimating a remainingamount of a battery, or a SOC, by integrating a current amount to becharged or discharged through a current sensor disposed at an end of thebattery.

In a next time period, the target battery from which the stateinformation is estimated based on the electrochemical model may switchto another battery. For example, a target battery set in a first timeperiod among a plurality of time periods may be set to be a nontargetbattery in a second time period, and a nontarget battery in the firsttime period may be set to be a target battery in the second time period.

As described above, by sequentially setting a target battery in a presetorder and estimating state information of a battery using theelectrochemical model, it is possible to effectively and rapidlyestimate a state of the battery with a relatively greater level ofaccuracy, without a burden of an amount of operations or calculations,even though using the electrochemical model that requires a relativelygreater amount of operations or calculations. This will be referred toas a cell switch model for the convenience of description. An example ofthe cell switch model will be described in greater detail below, withreference to FIGS. 4 and 5.

FIG. 4 illustrates an example of a process by which a battery stateestimating apparatus determines state information of a target battery ineach of a plurality of time periods.

Referring to FIG. 4, a battery 1 is set to be a target battery in timeperiod T1, and a sensed voltage 410 of the battery 1 is input to anelectrochemical model, and then state information of the battery 1 isestimated by the electrochemical model. When a time period changes fromtime period T1 to time period T2, the target battery switches from thebattery 1 to the battery 2, and the electrochemical model receives asensed voltage 420 of the battery 2 instead of the sensed voltage 410 ofthe battery 1. That is, at a switching time, there may be adiscontinuity between the sensed voltage 410 and the sensed voltage 420to be input to the electrochemical model. In the presence of suchdiscontinuity, when the electrochemical model derives and outputs stateinformation of the battery 2 from the sensed voltage 420, the output ofthe electrochemical model may have a discontinuity on a boundary betweentime period T1 and time period T2 as illustrated in a graph 430. Thisdiscontinuity may be corrected because it may be applied as an initialerror of the electrochemical model when estimating the state informationof the battery 2. The output of the electrochemical model that iscorrected through correction to be described hereinafter may exhibit acontinuity as illustrated in a graph 440.

FIG. 5 is a flowchart illustrating an example of a process by which abattery state estimating apparatus determines state information of atarget battery using an electrochemical model when the target batteryswitches to another target battery. However, during a time in which thetarget battery does not switch to another battery in a cell switchmodel, a method of estimating a state of a battery described above withreference to FIG. 3 may be applied.

Referring to FIG. 5, in operation 510, the battery state estimatingapparatus collects data of a previous battery and a current battery. Thecurrent battery may be a battery that is selected as a target battery ina current time period, and the previous battery may refer to a batterythat was selected as a target battery in a previous time period. Forexample, the battery state estimating apparatus may collect sensing dataof the current battery, and collect sensing data and/or an estimatedvoltage of the previous battery. The sensing data may include, forexample, a sensed voltage, a sensed current, and a sensed temperature,and may be stored in a form of a profile indicating a change inmagnitude over time. The estimated voltage of the previous battery maybe a voltage of the previous battery that is estimated by anelectrochemical model in the previous time period.

In operation 520, the battery state estimating apparatus calculates avoltage difference between the sensed voltage of the current battery (orthe target battery) and the sensed voltage or the estimated voltage ofthe previous battery. For example, the voltage difference may bedetermined to be a moving average voltage for a latest preset timeperiod.

Although not illustrated in FIG. 5, the battery state estimatingapparatus determines whether state information of a battery needs to becorrected based on whether a calculated voltage difference exceeds athreshold voltage difference. Although a target battery does not switch,an estimated voltage may differ from a sensed voltage of the targetbattery due to an error occurring in the electrochemical model. Thus,whether such correction is needed or not may be determined based on thecalculated voltage difference to prevent errors from being accumulated.

For example, when the calculated voltage difference exceeds thethreshold voltage difference, the battery state estimating apparatus maydetermine that the state information of the battery needs to becorrected, and may perform operation 530. In contrast, when thecalculated voltage difference does not exceed the threshold voltagedifference, the battery state estimating apparatus may determine thatthe state information of the battery does not need to be corrected, andmay estimate state information of the target battery using theelectrochemical model without performing operations 530 and 540.

In operation 530, the battery state estimating apparatus determines astate variation of the current battery (or the target battery) using thecalculated voltage difference. For example, the battery state estimatingapparatus may determine the state variation of the current battery basedon the calculated voltage difference, previous state information that isestimated in the previous time period, and an OCV table. The previousstate information may be state information estimated by theelectrochemical model from the previous battery that was selected as thetarget battery in the previous time period. The previous batteryselected as the target battery in the previous time period may be anontarget battery in the current time period. The state variation of thecurrent battery may include an SOC variation, which will be described ingreater detail hereinafter with reference to FIGS. 7 and 8.

In operation 540, the battery state estimating apparatus updates theelectrochemical model by correcting an internal state of theelectrochemical model based on the state variation of the currentbattery. For example, the battery state estimating apparatus may updatethe internal state of the electrochemical model by correcting an ionconcentration distribution in an active material particle or an ionconcentration distribution in an electrode based on the state variationof the current battery. The updating will be described in detailhereinafter with reference to FIGS. 9 through 11.

The battery state estimating apparatus may estimate state information ofthe current battery using the electrochemical model of which theinternal state is updated. In addition, the battery state estimatingapparatus may estimate, based on the electrochemical model, a voltage ofthe current battery or the target battery, or other properties such as,but not limited to, properties related to or indicative of a voltage ofthe current battery or the target battery.

FIG. 6 illustrates an example of an electrochemical model.

Referring to FIG. 6, an electrochemical model may estimate a remainingamount, or an SOC, of a battery by modeling an internal physicalphenomenon of the battery, for example, an ion concentration, apotential, and the like of the battery. That is, the electrochemicalmodel may be represented by a physical conservation equation associatedwith an electrochemical reaction occurring on an electrode/electrolyteinterface, an ion concentration of an electrode and an electrolyte, andconservation of electrical charges. For the physical conservationequation, the electrochemical model may use various model parameters,for example, a shape (e.g., thickness, radius, etc.), an open-circuitpotential (OCP), and a physical property value (e.g., electricalconductance, ionic conductance, diffusion coefficient, etc.).

In the electrochemical model, various state variables such as, forexample, a concentration and a potential, may be coupled to one another.An estimated voltage 610 that is estimated by the electrochemical modelmay indicate a potential difference between both ends, which are apositive electrode and a negative electrode. As illustrated by referencenumeral 620, potential information of each of the positive electrode andthe negative electrode may be affected by an ion concentrationdistribution of each of the positive electrode and the negativeelectrode. An SOC 630 to be estimated by the electrochemical model mayindicate an average ion concentration of the positive electrode and thenegative electrode.

The ion concentration distribution described above may be an ionconcentration distribution 640 in an electrode or an ion concentrationdistribution 650 in an active material particle present at a certainposition in the electrode. The ion concentration distribution 640 in theelectrode may be a surface ion concentration distribution or an averageion concentration distribution of an active material particle positionedin an electrode direction. The electrode direction may be a directionconnecting one end of the electrode, for example, a boundary adjacent toa current collector, and another end of the electrode, for example, aboundary adjacent to a separator. In addition, the ion concentrationdistribution 650 in the active material particle may be an ionconcentration distribution inside the active material particle based ona center direction of the active material particle. The center directionof the active material particle may be a direction connecting a centerof the active material particle and a surface of the active materialparticle.

As described above, to reduce a voltage difference between a sensedvoltage of a battery and an estimated voltage of the battery, a batterystate estimating apparatus may move or change an ion concentrationdistribution of each of a positive electrode and a negative electrodewhile maintaining physical conservation associated with a concentration,obtain potential information of each of the positive electrode and thenegative electrode based on the moved ion concentration distribution,and calculate a voltage based on the obtained potential information. Thebattery state estimating apparatus may calculate an internal statevariation at which the voltage difference is to be 0 and finallydetermine an SOC of the battery.

FIGS. 7 and 8 illustrate examples of determining a state variation of abattery.

FIG. 7 illustrates an example of determining a state variation of abattery in a case in which a sensed voltage of the battery is greaterthan an estimated voltage of the battery that is estimated by anelectrochemical model. The estimated voltage may be a voltage of thebattery that is estimated in a previous time period in a case of asingle cell model described above with reference to FIG. 3.Alternatively, the estimated voltage may be an estimated voltage of aprevious battery selected as a target battery in a previous time periodin a case of a cell switch model described above with reference to FIGS.4 through 6.

In an example, an OCV table indicates an SOC-OCV curve indicating anintrinsic characteristic of a battery. When using the OCV table, ΔSOC tobe corrected may vary according to an SOC value, and SOC information ofa previous time period that is lastly (e.g., most recently) estimatedmay be used. In a case of the single cell model described above withreference to FIG. 3, the SOC information of the previous time period maybe an estimated SOC of the battery in the previous time period. In acase of the cell switch model described above with reference to FIGS. 4through 6, the SOC information of the previous time period may be anestimated SOC of a previous battery selected as a target battery in theprevious time period.

An estimated OCV, which is an OCV corresponding to the SOC informationof the previous time period, may be obtained through the characteristiccurve of the OCV table illustrated in FIG. 7. A previously calculatedvoltage difference may be applied to the estimated OCV. This examplepertains to a case in which a sensed voltage is greater than anestimated voltage, and thus the calculated voltage difference may beapplied by adding the calculated voltage difference to the estimatedOCV. Using the characteristic curve of the OCV table, a corrected SOCcorresponding to a result of applying the calculated voltage differencemay be determined, and a difference between the estimated SOC and thecorrected SOC may be determined to be ΔSOC which indicates a statevariation.

FIG. 8 illustrates an example of determining a state variation of abattery in a case in which a sensed voltage of the battery is less thanan estimated voltage of the battery that is estimated by anelectrochemical model. The estimated voltage may be a voltage of thebattery that is estimated in a previous time period in a case of asingle cell model described above with reference to FIG. 3.Alternatively, the estimated voltage may be an estimated voltage of aprevious battery selected as a target battery in a previous time periodin a case of a cell switch model described above with reference to FIGS.4 through 6.

As described above, when using an OCV table, SOC information of aprevious time period that is lastly estimated may be used. In a case ofthe single cell model described above with reference to FIG. 3, the SOCinformation of the previous time period may be an estimated SOC of thebattery in the previous time period. In a case of the cell switch modeldescribed above with reference to FIGS. 4 through 6, the SOC informationof the previous time period may be an estimated SOC of a previousbattery selected as a target battery in the previous time period.

An estimated OCV, which is an OCV corresponding to the SOC informationof the previous time period, may be obtained through the characteristiccurve of the OCV table shown in FIG. 8. A previously calculated voltagedifference may be applied. This example pertains to a case in which asensed voltage is less than an estimated voltage, and thus thecalculated voltage difference may be applied by subtracting thecalculated voltage difference from the estimated OCV. Using thecharacteristic curve of the OCV table, a corrected SOC corresponding toa result of applying the calculated voltage difference may bedetermined, and a difference between the estimated SOC and the correctedSOC may be determined to be ΔSOC which indicates a state variation.

FIGS. 9 through 11 illustrate examples of updating an internal state ofan electrochemical model.

In an example, a battery state estimating apparatus may update aninternal state of an electrochemical model based on a state variation ofa battery. The electrochemical model may be configured to model aninternal physical phenomenon of the battery and estimate stateinformation of the battery. The internal state of the electrochemicalmodel may be provided in a form of a profile and may include, forexample, a voltage, an overpotential, an SOC, a positive electrodelithium ion concentration distribution, a negative electrode lithium ionconcentration distribution, and/or an electrolyte lithium ionconcentration distribution. For example, the battery state estimatingapparatus may update the internal state of the electrochemical model bycorrecting an ion concentration distribution in an active materialparticle or an ion concentration distribution in an electrode based onthe state variation of the battery. A more detailed description ofupdating an internal state of an electrochemical model will followhereinafter with reference to FIGS. 9 through 11.

FIG. 9 illustrates an example of updating an internal state of anelectrochemical model by uniformly correcting an ion concentrationdistribution. In this example, the ion concentration distribution mayindicate an ion concentration distribution in an active materialparticle or an ion concentration distribution in an electrode. Forexample, when a graph illustrated in FIG. 9 indicates the ionconcentration distribution in the active material particle, a horizontalaxis of the graph indicates a location in the active material particle.In this example, 0 indicates a center of the active material particleand 1 indicates a surface of the active material particle. For anotherexample, when the graph illustrated in FIG. 9 indicates the ionconcentration distribution in the electrode, a horizonal axis of thegraph indicates a location in the electrode. In this example, 0indicates one end of the electrode (e.g., a boundary adjacent to acollector) and 1 indicates another end of the electrode (e.g., aboundary adjacent to a separator).

The battery state estimating apparatus may convert a state variation ofa battery to a variation of an internal state, and uniformly apply thevariation to the internal state of the electrochemical model. Thevariation of the internal state may indicate a lithium ion concentrationvariation which corresponds to an area 910 between an initial internalstate and an updated internal state. Such a method of uniformly updatingthe internal state may be applied when a current output from the batteryis not large under the assumption that a concentration variation isuniform or consistent. The method may be simpler in implementationcompared to a nonuniform updating method to be described hereinafter.

Alternatively, when updating the internal state in which a lithium ionconcentration increases in an active material of one of a positiveelectrode and a negative electrode, the internal state may be updatedsuch that a lithium ion concentration in an active material of the otherone of the positive electrode and the negative electrode decreases by anincrement of the increase in the lithium ion concentration in the activematerial in the one electrode.

FIGS. 10a and 10b illustrate an example of updating an internal state ofan electrochemical model by nonuniformly correcting an ion concentrationdistribution. In this example, the ion concentration distribution mayindicate an ion concentration distribution in an active materialparticle or an ion concentration distribution in an electrode. Forexample, when graphs illustrated in FIGS. 10a and 10b indicate the ionconcentration distribution in the active material particle, a horizontalaxis of the graphs indicates a location in the active material particle.In this example, 0 indicates a center of the active material particle,and 1 indicates a surface of the active material particle. For anotherexample, when the graphs illustrated in FIGS. 10a and 10b indicate theion concentration distribution in the electrode, the horizontal axis ofthe graphs indicates a location in the electrode. In this example, 0indicates one end of the electrode (e.g., a boundary adjacent to acollector) and 1 indicates another end of the electrode (e.g., aboundary adjacent to a separator).

For example, when a conductance is substantially lowered, a current of abattery is relatively great, and/or a temperature of the battery isrelatively low, an internal diffusion characteristic may be weakenedbased on a chemical characteristic of the battery, and, accordingly, agradient of the ion concentration distribution may increase in anelectrode direction. In this example, based on the internal diffusioncharacteristic of the battery, the internal state of the electrochemicalmodel may be nonuniformly updated at each location in the activematerial particle or each location in the electrode.

A lithium ion may move in the battery based on the diffusioncharacteristic. For example, when a lithium ion of the positiveelectrode moves to the negative electrode, a lithium ion that is locatednearest to the negative electrode among lithium ions of the positiveelectrode may move first. In this example, when the internal diffusioncharacteristic of the battery is worse than before, lithium ions maymove considerably slowly in the positive electrode and a spot of thelithium ion moved out to the negative electrode may not be rapidlyfilled, and thus only a lithium ion located at an end of the positiveelectrode may continuously move out to the negative electrode and agradient of the ion concentration distribution may increase asillustrated in the graph of FIG. 10a . In contrast, when the internaldiffusion characteristic of the battery is better than before, a lithiumion located in the positive electrode may rapidly move to an end to fillthe spot of the lithium ion moved out to the negative electrode, andthus the gradient of the ion concentration distribution may decrease asillustrated in the graph of FIG. 10b . As described above with referenceto FIG. 9, an area between an initial internal state and an updatedinternal state, for example, an area 1010 of FIG. 10a and an area 1020of FIG. 10b , may correspond to a lithium ion concentration variation.Such diffusion characteristic as described above may be based on stateinformation (e.g., SOC) of a battery, and thus a diffusioncharacteristic based on a state variation of the battery may beconsidered. A more detailed description of consideration of a diffusioncharacteristic based on a state variation of the battery will follow.

The battery state estimating apparatus may determine a concentrationgradient characteristic based on the diffusion characteristic based onthe state variation of the battery, and update the internal state of theelectrochemical model based on the determined concentration gradientcharacteristic. A diffusion coefficient may be derived based on ananalysis of a diffusion characteristic of a state direction in which alithium ion is to move, for example, a direction in which a lithium ionconcentration increases. For example, a diffusion coefficient based on aprevious SOC and a diffusion coefficient of an SOC to move may bederived. In addition, the internal state of the electrochemical modelmay be updated based on a concentration gradient characteristic set inadvance based on the diffusion coefficient. For example, when thediffusion coefficient decreases in a direction for the movement, theinternal state of the electrochemical model may be updated in adirection in which the concentration gradient increases. In contrast,when the diffusion coefficient increases in the direction for themovement, the internal state of the electrochemical model may be updatedin a direction in which the concentration gradient decreases.

In another example, the electrochemical model may be a model based on aprinciple that a total amount of lithium ions is constantly conserved,although the lithium ions may move among the positive electrode, thenegative electrode, and an electrolyte. Such movement of the lithiumions among the positive electrode, the negative electrode, and theelectrolyte may be obtained based on a diffusion equation, which will bedescribed in greater detail hereinafter.

The battery state estimating apparatus may calculate a diffusionequation of an active material based on the state variation of thebattery, and update the internal state of the electrochemical model. Thebattery state estimating apparatus may assign a current boundarycondition in a state direction to which a lithium ion is to move, forexample, a direction in which a lithium ion concentration increases, tocalculate the diffusion equation, and update the internal state of theelectrochemical model. The battery state estimating apparatus maycalculate the diffusion equation of the active material with respect toa variation of the internal state corresponding to the state variationof the battery, and update the internal state of the electrochemicalmodel with an ion concentration distribution that is calculated throughthe diffusion equation. The diffusion characteristic is one physicalcharacteristic among a plurality of physical characteristics, and thusthe battery state estimating apparatus may nonuniformly update theinternal state of the electrochemical model by calculating the diffusionequation with respect to the ion concentration distribution.

FIG. 11 is a flowchart illustrating an example of a method of estimatinga state of a battery. The example method of estimating a state of abattery will be hereinafter simply referred to as a battery stateestimating method.

The battery state estimating method to be described hereinafter withreference to FIG. 11 may be performed, for example, by a processorincluded in a battery state estimating apparatus.

Referring to FIG. 11, in operation 1110, the battery state estimatingapparatus determines a state variation of a battery using a voltagedifference between a sensed voltage of the battery and an estimatedvoltage of the battery that is estimated by an electrochemical model.The battery state estimating apparatus may determine the state variationof the battery based on the voltage difference, previous stateinformation that is previously estimated by the electrochemical model,and an OCV table. For example, the battery state estimating apparatusmay determine the state variation of the battery by obtaining an OCVcorresponding to the previous state information based on the OCV table,and applying the voltage difference to the obtained OCV.

In operation 1120, the battery state estimating apparatus updates aninternal state of the electrochemical model based on the state variationof the battery. The battery state estimating apparatus may update theinternal state of the electrochemical model by correcting an ionconcentration distribution in an active material particle or an ionconcentration distribution in an electrode based on the state variationof the battery. For example, the battery state estimating apparatus mayupdate the internal state of the electrochemical model by uniformlycorrecting the ion concentration distribution in the active materialparticle or the ion concentration distribution in the electrode to beconsistent based on the state variation of the battery. In addition, thebattery state estimating apparatus may update the internal state of theelectrochemical model by determining a concentration gradientcharacteristic based on a diffusion characteristic based on the statevariation of the battery, and correcting the ion concentrationdistribution in the active material particle or the ion concentrationdistribution in the electrode based on the determined concentrationgradient characteristic. In addition, the battery state estimatingapparatus may update the internal state of the electrochemical model bycalculating a diffusion equation of an active material based on thestate variation of the battery, and correcting the ion concentrationdistribution in the active material particle or the ion concentrationdistribution in the electrode.

In operation 1130, the battery state estimating apparatus estimatesstate information of the battery based on the internal state of theelectrochemical model.

According to an example, before performing operation 1110, the batterystate estimating apparatus may verify whether the voltage differencebetween the sensed voltage of the battery and the estimated voltage ofthe battery exceeds a threshold voltage difference. When the voltagedifference exceeds the threshold voltage difference, the battery stateestimating apparatus may perform operations 1110 through 1130.

For a more detailed description of examples of the battery stateestimation method, reference may be made to the descriptions providedabove with reference to FIGS. 1 through 10.

FIGS. 12a and 12b illustrate another example of a battery stateestimating method.

FIG. 12a is a flowchart illustrating another example of a battery stateestimating method to be performed by a processor included in a batterystate estimating apparatus. A state of a battery may be estimated in aplurality of time periods. In the example of FIG. 12a , stateinformation of the battery may be estimated in each of the time periods.

Referring to FIG. 12a , in operation 1210, the battery state estimatingapparatus collects sensing data of a battery. The sensing data mayinclude, for example, a sensed voltage, a sensed current, and a sensedtemperature of the battery.

In operation 1220, an estimated voltage of the battery and stateinformation, for example, an SOC, of the battery is determined by anelectrochemical model to which a sensed current and a sensed temperatureare input.

In operation 1230, the battery state estimating apparatus calculates afirst voltage difference between a voltage of the battery sensed in acurrent time period and a voltage of the battery estimated by theelectrochemical model in the current time period. For example, the firstvoltage difference may be determined to be a moving average voltage fora latest preset time period.

Although not illustrated in FIG. 12a , according to an example, thebattery state estimating apparatus may determine whether stateinformation of the battery needs to be corrected based on whether thecalculated first voltage difference exceeds a first threshold voltagedifference. When an error occurs in the electrochemical model, theestimated voltage, which is a voltage estimated using theelectrochemical model, may differ from the sensed voltage of thebattery. Thus, to prevent errors from being accumulated, the batterystate estimating apparatus may determine whether the correcting isneeded based on the calculated first voltage difference.

For example, when the calculated first voltage difference exceeds thefirst threshold voltage difference, the battery state estimatingapparatus determines that the state information of the battery needs tobe corrected and performs operation 1240. In contrast, for example, whenthe calculated first voltage difference does not exceed the firstthreshold voltage difference, the battery state estimating apparatusdetermines that the state information of the battery does not need to becorrected and returns to operation 1210 in a next time period.

In operation 1240, the battery state estimating apparatus selects atleast one correction method from an ion concentration correction methodand a microcurrent correction method. For example, when the calculatedfirst voltage difference exceeds a preset second threshold voltagedifference, the battery state estimating apparatus may select the ionconcentration correction method, and, otherwise, may select themicrocurrent correction method. Alternatively, the battery stateestimating apparatus may select at least one correction method fromamong the ion concentration correction method and the microcurrentcorrection method such that the ion concentration correction method isperformed for each first period and the microcurrent correction methodis performed for each second period. In such an example, the firstperiod may be longer than the second period. That is, the microcurrentcorrection method may be more frequently performed than the ionconcentration correction method.

In operation 1250, when the ion concentration correction method isselected, the battery state estimating apparatus corrects an internalstate of the electrochemical model using a state variation of thebattery that is determined by the calculated first voltage difference.For a more detailed description of an example of the ion concentrationcorrection method, reference may be made to the descriptions providedabove with reference to FIGS. 1 through 11.

In operation 1260, when the microcurrent correction method is selected,the battery state estimating apparatus corrects a sensed current of thebattery in the current time period that is to be input to theelectrochemical model, using a capacity error corresponding to a secondvoltage difference between a sensed voltage of the battery in a previoustime period and an estimated voltage in the previous time period. Themicrocurrent correction method will be described in detail hereinafter.

The battery state estimating apparatus may receive the sensed current ofthe battery in the current time period, and may determine a correctionvalue using a capacity error corresponding to a voltage difference ofthe battery in the previous time period. The battery state estimatingapparatus may then correct the sensed current using the correctionvalue. The corrected sensed current may then be input to theelectrochemical model.

The voltage difference in the previous time period may be a differencebetween the estimated voltage of the battery in the previous time periodand the sensed voltage of the battery in the previous time period.

The capacity error may be determined based on the voltage difference inthe previous time period and an estimated OCV in the previous timeperiod. An example of a manner by which the capacity error may bedetermined will be described hereinafter by referring to an OCV tableillustrated in FIG. 12b , for convenience of description.

Referring to FIG. 12b , a first SOC 1204 is determined. The first SOC1204 corresponds to a first OCV 1203 obtained by subtracting a value αof a portion of a voltage difference ΔV 1202 from an estimated OCV 1201,which is an estimated OCV in a previous time period. In addition, asecond SOC 1206 is determined. The second SOC 1206 corresponds to asecond OCV 1205 obtained by adding a value β of a remining portion ofthe voltage difference ΔV 1202 to the estimated OCV 1201. A statedifference ΔSOC 1207 between the first SOC 1204 and the second SOC 1206is multiplied by a capacity of the battery, and then the capacity errormay be determined.

The correction value may be determined as a value obtained by applying aweight to the capacity error and dividing, by a constant value, thecapacity error to which the weight is applied. The weight may bedetermined based on an average current value to be calculated based on asensed current in a current time period and/or a sensed current in aprevious time period. For example, when the average current value isrelatively great, the weight may be determined to be relatively small.In contrast, when the average current value is relatively small, theweight may be determined to be relatively great. The constant value mayindicate a state information updating period, for example, a length of acertain period.

An example of the microcurrent correction method is described in greaterdetail in U.S. Patent Application Publication No. 2018-0143254, theentire disclosure of which is incorporated herein by reference.

In operation 1270, the battery state estimating apparatus determineswhether to terminate an operation of estimating a state of the battery.For example, when a preset operation period has not elapsed, operation1210 may be performed in a next time period. In contrast, when thepreset operation period has elapsed, the operation of estimating a stateof the battery may be terminated.

FIG. 13 illustrates an example of a battery state estimating apparatus.

Referring to FIG. 13, a battery state estimating apparatus 1300 mayinclude, for example, a memory 1310 and a processor 1320. The memory1310 and the processor 1320 may communicate with each other through abus 1330.

The memory 1310 may include computer-readable instructions. When aninstruction stored in the memory 1310 is executed by the processor 1320,the processor 1320 may perform one or more, or all, of operations ormethods described above. The memory 1310 may be a volatile ornonvolatile memory.

The processor 1320 may execute instructions or programs, or control thebattery state estimating apparatus 1300. The processor 1320 maydetermine a state variation of a battery using a voltage differencebetween a sensed voltage of the battery and an estimated voltage of thebattery that is estimated by an electrochemical model, update aninternal state of the electrochemical model based on the determinedstate variation, and estimate state information of the battery based onthe updated internal state of the electrochemical model.

In addition, the battery state estimating apparatus 1300 may perform theoperations or methods described above.

FIGS. 14 and 15 illustrate an example of a vehicle.

Referring to FIG. 14, a vehicle 1400 may include, for example, a batterypack 1410 and a battery management system (BMS) 1420. The vehicle 1400may use the battery pack 1410 as a power source. The vehicle 1400 may bean electric vehicle or a hybrid vehicle, for example.

The battery pack 1410 may include a plurality of battery modules eachincluding a plurality of battery cells.

The BMS 1420 may monitor whether an abnormality occurs in the batterypack 1410, and prevent the battery pack 1410 from being over-charged orover-discharged. In addition, when a temperature of the battery pack1410 exceeds a first temperature, for example, 40° C. or is less than asecond temperature, for example, −10° C., the BMS 1420 may performthermal control on the battery pack 1410. In addition, the BMS 1420 mayperform cell balancing to equalize charging states of the battery cellsincluded in the battery pack 1410.

In an example, the BMS 1420 may include a battery state estimatingapparatus described above, and determine state information of each ofthe battery cells included in the battery pack 1410 or state informationof the battery pack 1410. The BMS 1420 may determine, to be the stateinformation of the battery pack 1410, a maximum value, a minimum value,or an average value of the state information the battery cells.

The BMS 1420 may transmit the state information of the battery pack 1410to an electronic control unit (ECU) or a vehicle control unit (VCU) ofthe vehicle 1400. The ECU or the VCU of the vehicle 1400 may output thestate information of the battery pack 1410 to a display of the vehicle1400.

As illustrated in FIG. 15, the ECU or the VCU may display the stateinformation of the battery pack 1410 on a dashboard 1510. Alternatively,the ECU or the VCU may display, on the dashboard 1510, a remainingavailable travel distance determined based on the estimated stateinformation. Alternatively or additionally, the ECU or the VCU maydisplay the state information, the remaining available travel distance,and the like on a head-up display of the vehicle 1400.

For a more detailed description of example features and operations ofthe vehicle 1400, reference may be made to the descriptions providedabove with reference to FIGS. 1 through 13, and a more detailed andrepeated description will be omitted here for brevity.

FIG. 16 illustrates an example of a mobile device.

Referring to FIG. 16, a mobile device 1600 includes a battery pack 1610.The mobile device 1600 may use the battery pack 1610 as a power source.The mobile device 1600 may be a portable terminal such as a smartphone.FIG. 16 illustrates the mobile device 1600 as a smartphone as an examplefor the convenience of description. However, the mobile device 1600 maybe other terminals, for example, a laptop computer, a tablet personalcomputer (PC), a wearable device, and the like. The battery pack 1610may include a BMS and battery cells (or battery modules).

In an example, the mobile device 1600 may include a battery stateestimating apparatus described above. The battery state estimatingapparatus may update an internal state of an electrochemical model basedon a state variation of the battery pack 1610 or the battery cellsincluded in the battery pack 1610, and may estimate state information ofthe battery pack 1610 based on the updated internal state of theelectrochemical model.

For a more detailed description of example features and operations ofthe mobile device 1600, reference may be made to the descriptionsprovided above with reference to FIGS. 1 through 15 and a more detailedand repeated description will be omitted here for brevity.

The battery apparatuses, the battery apparatus 100, the battery stateestimating apparatuses, the battery state estimating apparatuses 120 and1300, the memories, the memory 1310, the processors, the processor 1320,the bus 1330, the BMSs, the BMS 1420, the ECU, and the VCU in FIGS. 1-16that perform the operations described in this application areimplemented by hardware components configured to perform the operationsdescribed in this application that are performed by the hardwarecomponents. Examples of hardware components that may be used to performthe operations described in this application where appropriate includecontrollers, sensors, generators, drivers, memories, comparators,arithmetic logic units, adders, subtractors, multipliers, dividers,integrators, and any other electronic components configured to performthe operations described in this application. In other examples, one ormore of the hardware components that perform the operations described inthis application are implemented by computing hardware, for example, byone or more processors or computers. A processor or computer may beimplemented by one or more processing elements, such as an array oflogic gates, a controller and an arithmetic logic unit, a digital signalprocessor, a microcomputer, a programmable logic controller, afield-programmable gate array, a programmable logic array, amicroprocessor, or any other device or combination of devices that isconfigured to respond to and execute instructions in a defined manner toachieve a desired result. In one example, a processor or computerincludes, or is connected to, one or more memories storing instructionsor software that are executed by the processor or computer. Hardwarecomponents implemented by a processor or computer may executeinstructions or software, such as an operating system (OS) and one ormore software applications that run on the OS, to perform the operationsdescribed in this application. The hardware components may also access,manipulate, process, create, and store data in response to execution ofthe instructions or software. For simplicity, the singular term“processor” or “computer” may be used in the description of the examplesdescribed in this application, but in other examples multiple processorsor computers may be used, or a processor or computer may includemultiple processing elements, or multiple types of processing elements,or both. For example, a single hardware component or two or morehardware components may be implemented by a single processor, or two ormore processors, or a processor and a controller. One or more hardwarecomponents may be implemented by one or more processors, or a processorand a controller, and one or more other hardware components may beimplemented by one or more other processors, or another processor andanother controller. One or more processors, or a processor and acontroller, may implement a single hardware component, or two or morehardware components. A hardware component may have any one or more ofdifferent processing configurations, examples of which include a singleprocessor, independent processors, parallel processors,single-instruction single-data (SISD) multiprocessing,single-instruction multiple-data (SIMD) multiprocessing,multiple-instruction single-data (MISD) multiprocessing, andmultiple-instruction multiple-data (MIMD) multiprocessing.

The methods illustrated in FIGS. 1-16 that perform the operationsdescribed in this application are performed by computing hardware, forexample, by one or more processors or computers, implemented asdescribed above executing instructions or software to perform theoperations described in this application that are performed by themethods. For example, a single operation or two or more operations maybe performed by a single processor, or two or more processors, or aprocessor and a controller. One or more operations may be performed byone or more processors, or a processor and a controller, and one or moreother operations may be performed by one or more other processors, oranother processor and another controller. One or more processors, or aprocessor and a controller, may perform a single operation, or two ormore operations.

Instructions or software to control computing hardware, for example, oneor more processors or computers, to implement the hardware componentsand perform the methods as described above may be written as computerprograms, code segments, instructions or any combination thereof, forindividually or collectively instructing or configuring the one or moreprocessors or computers to operate as a machine or special-purposecomputer to perform the operations that are performed by the hardwarecomponents and the methods as described above. In one example, theinstructions or software include machine code that is directly executedby the one or more processors or computers, such as machine codeproduced by a compiler. In another example, the instructions or softwareincludes higher-level code that is executed by the one or moreprocessors or computer using an interpreter. The instructions orsoftware may be written using any programming language based on theblock diagrams and the flow charts illustrated in the drawings and thecorresponding descriptions in the specification, which disclosealgorithms for performing the operations that are performed by thehardware components and the methods as described above.

The instructions or software to control computing hardware, for example,one or more processors or computers, to implement the hardwarecomponents and perform the methods as described above, and anyassociated data, data files, and data structures, may be recorded,stored, or fixed in or on one or more non-transitory computer-readablestorage media. Examples of a non-transitory computer-readable storagemedium include read-only memory (ROM), random-access memory (RAM), flashmemory, CD-ROMs, CD-Rs, CD+Rs, CD-RWs, CD+RWs, DVD-ROMs, DVD-Rs, DVD+Rs,DVD-RWs, DVD+RWs, DVD-RAMs, BD-ROMs, BD-Rs, BD-R LTHs, BD-REs, magnetictapes, floppy disks, magneto-optical data storage devices, optical datastorage devices, hard disks, solid-state disks, and any other devicethat is configured to store the instructions or software and anyassociated data, data files, and data structures in a non-transitorymanner and provide the instructions or software and any associated data,data files, and data structures to one or more processors or computersso that the one or more processors or computers can execute theinstructions. In one example, the instructions or software and anyassociated data, data files, and data structures are distributed overnetwork-coupled computer systems so that the instructions and softwareand any associated data, data files, and data structures are stored,accessed, and executed in a distributed fashion by the one or moreprocessors or computers.

While this disclosure includes specific examples, it will be apparentafter an understanding of the disclosure of this application thatvarious changes in form and details may be made in these exampleswithout departing from the spirit and scope of the claims and theirequivalents. The examples described herein are to be considered in adescriptive sense only, and not for purposes of limitation. Descriptionsof features or aspects in each example are to be considered as beingapplicable to similar features or aspects in other examples. Suitableresults may be achieved if the described techniques are performed in adifferent order, and/or if components in a described system,architecture, device, or circuit are combined in a different manner,and/or replaced or supplemented by other components or theirequivalents. Therefore, the scope of the disclosure is defined not bythe detailed description, but by the claims and their equivalents, andall variations within the scope of the claims and their equivalents areto be construed as being included in the disclosure.

What is claimed is:
 1. A processor-implemented method with battery stateestimation, comprising: obtaining sensing data including a sensedvoltage of a battery; obtaining an estimated voltage of the battery fromthe sensing data using an electrochemical model corresponding to thebattery; calculating a first voltage difference between the sensedvoltage of the battery and the estimated voltage of the battery;selecting a correction method of the electrochemical model based on thefirst voltage difference; correcting an internal state of theelectrochemical model or a sensed current to be input to theelectrochemical model by applying the selected correction method to theelectrochemical model; and estimating state information of the batteryusing the electrochemical model to which the correction method isapplied.
 2. The method of claim 1, wherein the correcting of theinternal state of the electrochemical model or the sensed current to beinput to the electrochemical model based on the selected correctionmethod comprises: updating the internal state of the electrochemicalmodel using a state variation of the battery determined by the firstvoltage difference between a voltage of the battery sensed in a currenttime period and a voltage of the battery estimated by theelectrochemical model; or correcting a sensed current of the battery inthe current time period to be input to the electrochemical model using acapacity error corresponding to a second voltage difference between asensed voltage of the battery in a previous time period and an estimatedvoltage of the battery in the previous time period.
 3. The method ofclaim 1, wherein the selecting of the correction method of theelectrochemical model based on the first voltage difference comprises:in response to the first voltage difference being greater than athreshold voltage difference, selecting a correction method ofcorrecting the internal state of the electrochemical model; and inresponse to the first voltage difference being less than or equal to thethreshold voltage difference, selecting a correction method ofcorrecting the sensed current to be input to the electrochemical model.4. The method of claim 1, wherein the selecting of the correction methodof the electrochemical model comprises selecting the correction methodof the electrochemical model such that a correction method of correctingthe sensed current to be input to the electrochemical model is to beperformed more frequently than a correction method of correcting theinternal state of the electrochemical model.
 5. A non-transitorycomputer-readable storage medium storing instructions that, whenexecuted by a processor, cause the processor to perform the method ofclaim 1.